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Record W4210378879 · doi:10.1190/int-2021-0152.1

Evaluation method of hydrocarbon yield of source rocks in open, semiopen, and closed systems: A case study on the K1qn Formation, northern Songliao Basin, China

2022· article· en· W4210378879 on OpenAlexaff
Wenguang Wang, Min Wang, Shuangfang Lu, Chengyan Lin, Min Zheng

Bibliographic record

VenueInterpretation · 2022
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsPetro-Canada
FundersNational Natural Science Foundation of China
KeywordsSource rockHydrocarbonGeologyKerogenYield (engineering)CrackingFossil fuelBasin modellingStructural basinPetroleum engineeringMineralogyGeochemistryChemistryGeomorphologyEngineeringThermodynamicsWaste management

Abstract

fetched live from OpenAlex

Abstract Hydrocarbon yield of source rocks is an important parameter in the evaluation of oil and gas resources, and its value determines the potential of conventional and unconventional oil and gas resources. There is no oil cracking into gas in the thermal pyrolysis experiment data of an open system, whereas kerogen cracking into oil in a closed system is involved in the calculation of oil cracking into gas. However, most source rocks in sedimentary basins are a process of hydrocarbon generation and hydrocarbon expulsion, which could lead to insufficient understanding of hydrocarbon yield of source rocks. Based on the multiple thermal pyrolysis experiment data, three hydrocarbon generation kinetic models, and actual geologic data (burial history, thermal history, and hydrocarbon generation threshold), we established the evaluation method and chart of hydrocarbon yield of source rocks under open, semiopen, and closed systems by using the hydrocarbon generation kinetics method. The concept of degree of openness was proposed. From a closed system to an open system, the degree of openness increases gradually, and its value changes from 0 to 1. Taking the K1qn Formation in the northern Songliao Basin as an example, the hydrocarbon expulsion efficiency of the K1qn Formation source rock is approximately 70%, and it can be approximated as the degree of openness. Based on our method for evaluating hydrocarbon yield of source rocks, the charts of hydrocarbon yield of source rocks under open system, semiopen system with a degree of openness of 0.7, and closed system of the K1qn Formation in the northern Songliao Basin were established. The oil and gas yields of the K1qn Formation source rocks in a semiopen system with a degree of openness of 0.7 are approximately 540 and 105 mgHC/gTOC at a burial depth of 2000 m, respectively. Our results indicate that the hydrocarbon yield of source rocks in a semiopen system is closer to the hydrocarbon yield of source rocks under geologic condition. We use the thermal pyrolysis experiment data, hydrocarbon generation kinetics model, and geologic data to propose a very valuable evaluation method of hydrocarbon yield of source rock, which has a solid theoretical basis and strong applicability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.290
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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